1. Field
The present disclosure is directed to a method and apparatus for scheduling electronic delivery of multimedia content and advertisements targeted to customer premises equipment. More particularly, the present disclosure is directed to scheduling, pricing, and delivering subscriber-selected, preplanned multimedia content and advertisements targeted to customer premises equipment.
2. Introduction
Traditional television viewing is linear. With linear television viewing, a viewer must watch scheduled television programs at a particular time and on a particular channel. However, the entertainment industry is in transition and content consumers are moving to personalized programming or nonlinear television. Personalized programming allows the content consumer to control content selection and viewing time. True personalized programming allows content customers to view whatever they want, whenever they want, as often as they want, and in an order that the content consumers desire. However, present technology offers content consumers a limited personalized programming experience with restricted selection and/or high prices. Such present technology includes Over-The-Top (OTT) streaming, Internet Protocol Television (IPTV), and Video on Demand (VOD) capability.
OTT streaming is delivered over the Internet, but is unmanaged. Content delivery is plagued by “last mile” congestion. This problem is encountered with non-Quality of Service (QoS) streaming. As streaming has increased, especially during periods of peak demand or prime time, congestion impact has become worse. Such congestion occurs with any over utilized, unmanaged, data rate limited, network system. IPTV are multimedia services, such as television or video, delivered over managed IP based networks which provide the required level of Quality of Service (QoS) and experience, security, interactivity and reliability. From the consumer's viewpoint, IPTV has limited selection and is expensive.
Demand for content distribution with respect to available transport varies as a function of time, consisting of peaks and valleys. Traditionally, supply and demand issues have been addressed by applying the principles of microeconomics based on supply and demand pricing theory. However, applying such microeconomics has not solved problems associated with content delivery.
Stand Definition (SD), High Definition (HD) and Ultra High Definition (UHD or 4K), or higher resolution formats present increasingly significant problems for existing network infrastructure and significantly higher price to content consumers. Such high resolution formats are limited because of data rate constraints and subscriber aggregate data limits. By 2018, the number of households using streaming has been projected to increase to 50% and beyond. Even with the current relatively low percentage of streaming, peak time network congestion is causing video disruption, such as video pixilation, synchronization problems, freeze frames, etc. This video disruption will be furthered exacerbated as more households move to content streaming, higher resolution video, and as additional real-time services are further employed.
Advertising can be used as a subsidized approach to both linear and personalized programming streaming. However with current advertising/content ratios, bandwidth required to additionally transmit advertising content is increased by as much as 50%. Other challenges that effect high impact advertising exist for both linear and personalized programming. Currently, advertisers have traditionally subsidized content production and distribution through advertisements (for example, commercials). This approach has a limited capability to target an audience segment. Advertisers are unable to target advertisements to individuals or cluster groups except by relying on associated program content. Currently, commercial selection and insertion is dependent on the targeted audience of the content rather than the targeted audience of the commercial. True, direct measures of advertisement effectiveness do not exist. Changing commercials on-the-fly to reflect changes in consumer content viewing habits is difficult. Also, it is extremely difficult to measure the impact of on-the-fly advertising on individual subscribers, cluster groups, and/or geographic areas based on selected advertising profiles.